A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant?

Yifan Zhong; Tahir Lazaro Sousa Menezes; Vikas Kumar; Qian Zhao; F Maxwell Harper

doi:10.1145/3240323.3240395

A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant?

Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, F Maxwell Harper

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

5 Scopus citations

Abstract

Many video sites recommend videos related to the one a user is watching. These recommendations have been shown to influence what users end up exploring and are an important part of a recommender system. Plenty of methods have been proposed to recommend related videos, but there has been relatively little work that compares competing strategies. We describe a field study of related video recommendations, where we deploy algorithms to recommend related movie trailers. Our results show that recency- and similarity-based algorithms yield the highest click-through rates, and that the recency-based algorithm leads to the most trailer-level engagement. Our findings suggest the potential to design non-personalized yet effective related item recommendation strategies.

Original language	English (US)
Title of host publication	Proceedings of the 12th ACM Conference on Recommender Systems
Publisher	ACM
Pages	274-278
Number of pages	5
ISBN (Print)	978-1-4503-5901-6
DOIs	https://doi.org/10.1145/3240323.3240395
State	Published - 2018

Publication series

Name	Proceedings of the 12th ACM Conference on Recommender Systems

Keywords

field study
item similarity
movie trailers
recommender systems
related item recommendations

Publisher link

10.1145/3240323.3240395

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Cite this

A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant? / Zhong, Yifan; Menezes, Tahir Lazaro Sousa; Kumar, Vikas et al.
Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 2018. p. 274-278 (Proceedings of the 12th ACM Conference on Recommender Systems).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

@inbook{cdaf4185dea54357a374e7a39e5d3264,

title = "A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant?",

abstract = "Many video sites recommend videos related to the one a user is watching. These recommendations have been shown to influence what users end up exploring and are an important part of a recommender system. Plenty of methods have been proposed to recommend related videos, but there has been relatively little work that compares competing strategies. We describe a field study of related video recommendations, where we deploy algorithms to recommend related movie trailers. Our results show that recency- and similarity-based algorithms yield the highest click-through rates, and that the recency-based algorithm leads to the most trailer-level engagement. Our findings suggest the potential to design non-personalized yet effective related item recommendation strategies.",

keywords = "field study, item similarity, movie trailers, recommender systems, related item recommendations",

author = "Yifan Zhong and Menezes, {Tahir Lazaro Sousa} and Vikas Kumar and Qian Zhao and Harper, {F Maxwell}",

year = "2018",

doi = "10.1145/3240323.3240395",

language = "English (US)",

isbn = "978-1-4503-5901-6",

series = "Proceedings of the 12th ACM Conference on Recommender Systems",

publisher = "ACM",